Sparse Self-Calibration Imaging via Iterative MAP in FM-Based Distributed Passive Radar

Distributed passive radar imaging systems based on illuminators of opportunity such as frequency-modulation-based stations exhibit poor imaging performance owing to the narrow bandwidth of the radiated signals and the small number of illuminators. Moreover, the position errors of the illuminators and the receivers would further deteriorate the inversion performance. In this letter, the sparse self-calibration imaging via iterative maximum a posteriori probability method is proposed for simultaneous sparse imaging, self-calibrating, and parameter updating, which exploits the sparse priority of the target. Besides, the convergence and the initialization of the method are discussed. Numerical simulations verify the effectiveness of the proposed method and its analysis.